During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform…
International Journal of Multimedia Information Retrieval Template
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About the International Journal of Multimedia Information Retrieval format
International Journal of Multimedia Information Retrieval is a peer-reviewed journal published by Springer Nature, covering Advanced Image and Video Retrieval Techniques, Image Retrieval and Classification Techniques, Multimodal Machine Learning Applications.
| Publisher | Springer Nature |
|---|---|
| Reference style | Numbered (Springer Basic) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C (2023) A representative article title. International Journal of Multimedia Information Retrieval 12:45–58
Formats any DOI in International Journal of Multimedia Information Retrieval style. No sign-up. |
| Publishes research in | Advanced Image and Video Retrieval Techniques Image Retrieval and Classification Techniques Multimodal Machine Learning Applications Video Analysis and Summarization Human Pose and Action Recognition |
| ISSN | 2192-6611 |
| Citation impact (2-yr) | 4.6 |
| h-index | 41 |
| i10-index | 162 |
| Total citations | 8,697 |
| Article processing charge | $2,890 |
| Top institutions publishing here | Leiden University |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in International Journal of Multimedia Information Retrieval per year
Citation impact of International Journal of Multimedia Information Retrieval by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in International Journal of Multimedia Information Retrieval
Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial…